Accumulative evidence in the literature suggests alcohol consumption is a protective factor of the metabolic syndrome (MS). However, few studies investigated the relationship between alcohol consumption and components of MS. We examined association of several types of alcoholic beverage with components of MS among people in rural China. In the Nantong Metabolic Syndrome Study (NMSS), a cross-sectional study, a total of 20,502 participants, including 13,505 women and 6,997 men aged 18–74 years, were recruited between 2007 and 2008 in Nantong, China. Socio-economic status, dietary intake, physical exercise, alcoholic beverage consumption, and smoking status information were obtained, and triglycerides (TG), high-density lipoprtein cholesterol (HDL-c), blood pressure (BP) and blood glucose level were examined for all participants. Logistic regression model and the restricted cubic spline approach were used to analyze the associations between alcoholic beverage consumption and MS components. The MS prevalence was 21.1% in the whole population, which was significantly low among drinkers (20.6%), compared with non-drinkers (23.6%) in women, and was comparable in men (16.4% versus 17.1%). High HDL-c level was observed among drinkers, compared with non-drinkers in both men and women. Low TG level and Systolic BP (SBP) were found only among rice wine drinkers in women, and high waist circumference, high TG and BP were found among beer and liquor drinkers in men. Furthermore, we found that the highest quartile of rice wine drink in women may decrease 24% risk of high TG, 30% risk of low HDL-c and 43% risk of high glucose among MS components cases respectively, compared with non-drinkers (p for trend <0.01 for those three components). While compared non-drinkers among men, the highest quartile of liquor drink may increase 32% risk of high SBP, 55% risk of high Diastolic BP (DBP) and 34% risk of abdominal obesity among MS components cases respectively, but decrease 45% risk of low HDL-c (p for trend <0.05 for those four components). Our data suggested that all alcoholic beverages increased HDL-c level. Rice wine decreased both TG level and blood glucose in women only and it could be one of healthy alcoholic beverages in MS prevention in Chinese women. While excessive liquor consumption increased BP and waist circumference level and it may lead to hypertension and central obesity in Chinese men.

Several polymorphisms in the apolipoprotein C3 (APOC3) gene have been found association with hypertriglyceridemia(HTG), but the link with coronary heart disease(CHD) risk between ethnicities was still controversial. Among them, reseachers paid more attentions to the promoter polymorphisms T-455C and C-482T because both of them located in insulin-responsive element (IRE) and insulin was thought to exert its action by down-regulating APOC3 gene expression. The aim of this study was to investigate the association of the two polymorphisms of APOC3 with CHD in a Han population in East China. TaqMan SNP Genotyping Assays were carried out to detect the genotypes of APOC3 gene, including the T-455C and C-482T, in 286 subjects with CHD and 325 controls without CHD. The levels of serum lipid profiles were also detected by biochemical methods. There was no difference of genotype frequencies and allele frequencies between the CHD population and the controls(P > 0.05). Compared with the most common genotype -455TT or -482CC, the variants had neither significantly increased CHD risk, nor the lipid variables showed any statistically relevant differences in the research population. The adjusted OR of CHD were 5.67 [0.27-18.74] and 0.75 [0.20-2.73] in carriers of the APOC3 -455C and -482T variants, respectively(P > 0.05). There was also no significant difference in APOC3 haplotype distribution in CHD and controls, but there was a strong linkage disequilibrium between T-455C and C-482T with D' = 0.9293, 0.8881, respectively(P < 0.0001). Our data did not support a relationship between the two polymorphisms of APOC3 gene and risk of CHD in the Han population in East China.

In genome-wide association studies, linear mixed models (LMMs) have been widely used to explore the molecular mechanism of complex traits. However, typical association approaches suffer from several important drawbacks: estimation of variance components in LMMs with large scale individuals is computationally slow; single-locus model is unsatisfactory to handle complex confounding and causes loss of statistical power. To address these issues, we propose an efficient two-stage method based on hybrid of restricted and penalized maximum likelihood, named HRePML. Firstly, we performed restricted maximum likelihood (REML) on single-locus LMM to remove unrelated markers, where spectral decomposition on covariance matrix was used to fast estimate variance components. Secondly, we carried out penalized maximum likelihood (PML) on multi-locus LMM for markers with reasonably large effects. To validate the effectiveness of HRePML, we conducted a series of simulation studies and real data analyses. As a result, our method always had the highest average statistical power compared with multi-locus mixed-model (MLMM), fixed and random model circulating probability unification (FarmCPU), and genome-wide efficient mixed model association (GEMMA). More importantly, HRePML can provide higher accuracy estimation of marker effects. HRePML also identifies 41 previous reported genes associated with development traits in Arabidopsis, which is more than was detected by the other methods.

Obesity is a major global public health challenge caused by a complex interplay of genetic predisposition, environmental factors (notably dietary transitions), and their interactions. In 2025, China's National Health Commission (NHC) launched the “Year of Weight Management” initiative during its key policy-setting sessions, generating substantial public and scientific attention. Despite extensive research on obesity, a comprehensive analysis integrating gene–diet interactions with sex differences specifically in Chinese adults remains lacking. This review synthesizes recent advances in understanding obesity-related gene polymorphisms, dietary influences, and gene–diet interactions in Chinese adults, with particular emphasis on multigene synergistic effects. Our analysis demonstrates that such genetic synergism shows potential for predicting obesity intervention outcomes, while gene–diet interactions substantially contribute to obesity heterogeneity. Furthermore, maintaining a balanced dietary structure is particularly crucial for specific genotypes, with sex differences exerting distinct moderating effects. By transcending conventional single-factor analytical frameworks and incorporating population-specific genetic and dietary backgrounds, this study proposes that future research should prioritize developing a three-dimensional gene–diet–metabolic phenotype model (an integrative framework considering genotype, dietary intake, and dynamic metabolic outcomes) to identify critical windows and mechanisms of metabolic dysregulation. This approach holds substantial promise for informing personalized precision strategies for obesity prevention and control.